# Copyright 2018 The Cornac Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
# Copyright 2022 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import cornac
from cornac.eval_methods import RatioSplit
from cornac.models import VAECF
from cornac.metrics import Recall, NDCG
import torch.npu
import os

device_id=int(os.environ['ASCEND_DEVICE_ID'])
CALCULATE_DEVICE = "npu:{}".format(device_id)
torch.npu.set_device(CALCULATE_DEVICE)


data = cornac.datasets.movielens.load_feedback(variant="20M")

ratio_split = RatioSplit(
    data=data,
    test_size=0.2,
    exclude_unknowns=True,
    verbose=True,
    seed=123,
    rating_threshold=0.5,
)

# Instantiate the VAECF model
vaecf = cornac.models.VAECF(
    k=10,
    autoencoder_structure=[20],
    act_fn="tanh",
    likelihood="mult",
    n_epochs=1,
    batch_size=100,
    learning_rate=0.001,
    beta=1.0,
    #seed=123,
    verbose=True,
    use_gpu=True
)

# Instantiate evaluation measures
#metrics = [Recall(k=20),Recall(k=50),NDCG(k=100)]
rec_20 = cornac.metrics.Recall(k=20)
rec_50 = cornac.metrics.Recall(k=50)
ndcg_100 = cornac.metrics.NDCG(k=100)
#auc = cornac.metrics.AUC()

# Put everything together into an experiment and run it
cornac.Experiment(
    eval_method=ratio_split,
    models=[vaecf],
    metrics=[rec_20, rec_50, ndcg_100],
    user_based=True,
).run()
